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2020 – today
- 2024
[i35]Lei Xu, Sarah Alnegheimish, Laure Berti-Équille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers. CoRR abs/2401.17196 (2024)- [i34]Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni:
LLMs for XAI: Future Directions for Explaining Explanations. CoRR abs/2405.06064 (2024) - [i33]Sarah Alnegheimish, Linh Nguyen, Laure Berti-Équille, Kalyan Veeramachaneni:
Large language models can be zero-shot anomaly detectors for time series? CoRR abs/2405.14755 (2024) - 2023
- [i32]Sarah Alnegheimish, Laure Berti-Équille, Kalyan Veeramachaneni:
Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection. CoRR abs/2310.17748 (2023) - [i31]Alexandra Zytek, Wei-En Wang, Sofia Koukoura, Kalyan Veeramachaneni:
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring. CoRR abs/2312.02859 (2023) - [i30]Alexandra Zytek, Wei-En Wang, Dongyu Liu
, Laure Berti-Équille, Kalyan Veeramachaneni:
Pyreal: A Framework for Interpretable ML Explanations. CoRR abs/2312.13084 (2023) - 2022
- [j16]Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, Chengxiang Zhai, Kalyan Veeramachaneni:
AutoML to Date and Beyond: Challenges and Opportunities. ACM Comput. Surv. 54(8): 175:1-175:36 (2022) - [j15]Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni:
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 103:1-103:30 (2022) - [j14]Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Équille, Kalyan Veeramachaneni:
The Need for Interpretable Features: Motivation and Taxonomy. SIGKDD Explor. 24(1): 1-13 (2022) - [j13]Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni:
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models. IEEE Trans. Vis. Comput. Graph. 28(1): 378-388 (2022) - [j12]Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni:
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making. IEEE Trans. Vis. Comput. Graph. 28(1): 1161-1171 (2022) - [c76]Lawrence Wong, Dongyu Liu, Laure Berti-Équille, Sarah Alnegheimish, Kalyan Veeramachaneni:
AER: Auto-Encoder with Regression for Time Series Anomaly Detection. IEEE Big Data 2022: 1152-1161 - [c75]Lei Xu, Laure Berti-Équille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
In Situ Augmentation for Defending Against Adversarial Attacks on Text Classifiers. ICONIP (3) 2022: 485-496 - [c74]Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Équille, Kalyan Veeramachaneni:
R&R: Metric-guided Adversarial Sentence Generation. AACL/IJCNLP (Findings) 2022: 438-452 - [c73]Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Équille, Kalyan Veeramachaneni:
Sintel: A Machine Learning Framework to Extract Insights from Signals. SIGMOD Conference 2022: 1855-1865 - [i29]Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Équille, Kalyan Veeramachaneni:
The Need for Interpretable Features: Motivation and Taxonomy. CoRR abs/2202.11748 (2022) - [i28]Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Équille, Kalyan Veeramachaneni:
Sintel: A Machine Learning Framework to Extract Insights from Signals. CoRR abs/2204.09108 (2022) - [i27]Kevin Alex Zhang, Neha Patki, Kalyan Veeramachaneni:
Sequential Models in the Synthetic Data Vault. CoRR abs/2207.14406 (2022) - [i26]Lawrence Wong, Dongyu Liu, Laure Berti-Équille, Sarah Alnegheimish, Kalyan Veeramachaneni:
AER: Auto-Encoder with Regression for Time Series Anomaly Detection. CoRR abs/2212.13558 (2022) - 2021
- [j11]Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni:
Enabling Collaborative Data Science Development with the Ballet Framework. Proc. ACM Hum. Comput. Interact. 5(CSCW2): 431:1-431:39 (2021) - [c72]Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni:
Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making. CHI Extended Abstracts 2021: 315:1-315:6 - [c71]Yi Sun, Iván Ramírez Díaz, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Towards Reducing Biases in Combining Multiple Experts Online. IJCAI 2021: 3024-3030 - [i25]Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni:
Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making. CoRR abs/2103.02071 (2021) - [i24]Dongyu Liu, Kalyan Veeramachaneni, Alexander Geiger, Victor O. K. Li, Huamin Qu:
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data. CoRR abs/2103.12910 (2021) - [i23]Micah J. Smith, Jürgen Cito, Kalyan Veeramachaneni:
Meeting in the notebook: a notebook-based environment for micro-submissions in data science collaborations. CoRR abs/2103.15787 (2021) - [i22]Lei Xu, Kalyan Veeramachaneni:
Attacking Text Classifiers via Sentence Rewriting Sampler. CoRR abs/2104.08453 (2021) - [i21]Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni:
VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models. CoRR abs/2108.02550 (2021) - [i20]Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni:
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series. CoRR abs/2112.05734 (2021) - 2020
- [c70]Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. IEEE BigData 2020: 33-43 - [c69]Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni:
Understanding User-Bot Interactions for Small-Scale Automation in Open-Source Development. CHI Extended Abstracts 2020: 1-8 - [c68]Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, Kalyan Veeramachaneni:
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. DSAA 2020: 536-545 - [c67]Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni:
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development. SIGMOD Conference 2020: 785-800 - [i19]Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. CoRR abs/2009.07769 (2020) - [i18]Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, Kalyan Veeramachaneni:
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records. CoRR abs/2010.00509 (2020) - [i17]Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni:
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities. CoRR abs/2010.10777 (2020) - [i16]Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni:
Enabling collaborative data science development with the Ballet framework. CoRR abs/2012.07816 (2020)
2010 – 2019
- 2019
- [j10]Ignacio Arnaldo, Kalyan Veeramachaneni:
The Holy Grail of "Systems for Machine Learning": Teaming humans and machine learning for detecting cyber threats. SIGKDD Explor. 21(2): 39-47 (2019) - [c66]Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Learning Vine Copula Models for Synthetic Data Generation. AAAI 2019: 5049-5057 - [c65]Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu:
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. CHI 2019: 681 - [c64]Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni, Chengxiang Zhai:
TILM: Neural Language Models with Evolving Topical Influence. CoNLL 2019: 778-788 - [c63]Kevin Alex Zhang, Kalyan Veeramachaneni:
Enhancing Image Steganalysis with Adversarially Generated Examples. CSCML 2019: 169-177 - [c62]Ignacio Arnaldo, Kalyan Veeramachaneni, Mei Lam:
eX2: a framework for interactive anomaly detection. IUI Workshops 2019 - [c61]Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Modeling Tabular data using Conditional GAN. NeurIPS 2019: 7333-7343 - [i15]Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, Kalyan Veeramachaneni:
SteganoGAN: High Capacity Image Steganography with GANs. CoRR abs/1901.03892 (2019) - [i14]Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu:
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. CoRR abs/1902.05009 (2019) - [i13]Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni:
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development. CoRR abs/1905.08942 (2019) - [i12]Lei Xu, Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni:
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data. CoRR abs/1906.12348 (2019) - [i11]Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Modeling Tabular data using Conditional GAN. CoRR abs/1907.00503 (2019) - [i10]Yi Sun, Iván Ramírez, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Learning Fair Classifiers in Online Stochastic Settings. CoRR abs/1908.07009 (2019) - [i9]Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Robust Invisible Video Watermarking with Attention. CoRR abs/1909.01285 (2019) - 2018
- [c60]Ignacio Arnaldo, Ankit Arun, Sumeeth Kyathanahalli, Kalyan Veeramachaneni:
Acquire, adapt, and anticipate: continuous learning to block malicious domains. IEEE BigData 2018: 1891-1898 - [c59]Benjamin Schreck, Shankar Mallapur, Sarvesh Damle, Nitin John James, Sanjeev Vohra, Rajendra Prasad, Kalyan Veeramachaneni:
Augmenting Software Project Managers with Predictions from Machine Learning. IEEE BigData 2018: 2004-2011 - [c58]Zara Perumal, Kalyan Veeramachaneni:
Towards Building Active Defense Systems for Software Applications. CSCML 2018: 144-161 - [c57]Dennis G. Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo López Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Özcan, Sergio Iwan Valdez Pea, Brian Goldman, Salvador Botello Rionda, Arturo Hernández Aguirre, Kalyan Veeramachaneni, Sylvain Cussat-Blanc:
Summary of evolutionary computation for wind farm layout optimization. GECCO (Companion) 2018: 31-32 - [c56]Roy Wedge, James Max Kanter, Kalyan Veeramachaneni, Santiago Moral-Rubio, Sergio Iglesias Perez:
Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering. ECML/PKDD (3) 2018: 372-388 - [i8]James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni:
Machine learning 2.0 : Engineering Data Driven AI Products. CoRR abs/1807.00401 (2018) - [i7]Lei Xu, Kalyan Veeramachaneni:
Synthesizing Tabular Data using Generative Adversarial Networks. CoRR abs/1811.11264 (2018) - [i6]Gaurav Sheni, Benjamin Schreck, Roy Wedge, James Max Kanter, Kalyan Veeramachaneni:
Prediction Factory: automated development and collaborative evaluation of predictive models. CoRR abs/1811.11960 (2018) - [i5]Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Learning Vine Copula Models For Synthetic Data Generation. CoRR abs/1812.01226 (2018) - 2017
- [c55]Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, Kalyan Veeramachaneni:
ATM: A distributed, collaborative, scalable system for automated machine learning. IEEE BigData 2017: 151-162 - [c54]Ignacio Arnaldo, Alfredo Cuesta-Infante, Ankit Arun, Mei Lam, Costas Bassias, Kalyan Veeramachaneni:
Learning Representations for Log Data in Cybersecurity. CSCML 2017: 250-268 - [c53]Alec Anderson, Sébastien Dubois, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Sample, Estimate, Tune: Scaling Bayesian Auto-Tuning of Data Science Pipelines. DSAA 2017: 361-372 - [c52]Bennett Cyphers, Kalyan Veeramachaneni:
AnonML: Locally Private Machine Learning over a Network of Peers. DSAA 2017: 549-560 - [c51]Micah J. Smith, Roy Wedge, Kalyan Veeramachaneni:
FeatureHub: Towards Collaborative Data Science. DSAA 2017: 590-600 - [i4]Roy Wedge, James Max Kanter, Santiago Moral-Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni:
Solving the "false positives" problem in fraud prediction. CoRR abs/1710.07709 (2017) - 2016
- [c50]Kalyan Veeramachaneni, Ignacio Arnaldo, Vamsi Korrapati, Constantinos Bassias, Ke Li:
AI^2: Training a Big Data Machine to Defend. BigDataSecurity/HPSC/IDS 2016: 49-54 - [c49]Neha Patki, Roy Wedge, Kalyan Veeramachaneni:
The Synthetic Data Vault. DSAA 2016: 399-410 - [c48]James Max Kanter, Owen Gillespie, Kalyan Veeramachaneni:
Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering. DSAA 2016: 430-439 - [c47]Benjamin Schreck, Kalyan Veeramachaneni:
What Would a Data Scientist Ask? Automatically Formulating and Solving Predictive Problems. DSAA 2016: 440-451 - [c46]Sebastien Boyer, Kalyan Veeramachaneni:
Robust Predictive Models on MOOCs : Transferring Knowledge across Courses. EDM 2016: 298-305 - [c45]Ben U. Gelman, Matt Revelle, Carlotta Domeniconi, Kalyan Veeramachaneni, Aditya Johri:
Acting the Same Differently: A Cross-Course Comparison of User Behavior in MOOCs. EDM 2016: 376-381 - [c44]Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Markov Switching Copula Models for Longitudinal Data. ICDM Workshops 2016: 1104-1109 - [c43]Yuanzhe Chen, Qing Chen, Mingqian Zhao, Sebastien Boyer, Kalyan Veeramachaneni, Huamin Qu:
DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction. VAST 2016: 111-120 - 2015
- [j9]Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O'Reilly:
Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning. IEEE Comput. Intell. Mag. 10(1): 20-32 (2015) - [j8]Kalyan Veeramachaneni, Ignacio Arnaldo, Owen Derby, Una-May O'Reilly:
FlexGP - Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems. J. Grid Comput. 13(3): 391-407 (2015) - [c42]Sebastien Boyer, Kalyan Veeramachaneni:
Transfer Learning for Predictive Models in Massive Open Online Courses. AIED 2015: 54-63 - [c41]Franck Dernoncourt, Kalyan Veeramachaneni, Una-May O'Reilly:
Gaussian Process-Based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals. CBMS 2015: 145-150 - [c40]Sebastien Boyer, Ben U. Gelman, Benjamin Schreck, Kalyan Veeramachaneni:
Data science foundry for MOOCs. DSAA 2015: 1-10 - [c39]James Max Kanter, Kalyan Veeramachaneni:
Deep feature synthesis: Towards automating data science endeavors. DSAA 2015: 1-10 - [c38]Ignacio Arnaldo, Una-May O'Reilly, Kalyan Veeramachaneni:
Building Predictive Models via Feature Synthesis. GECCO 2015: 983-990 - [c37]Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly:
Copula Graphical Models for Wind Resource Estimation. IJCAI 2015: 2646-2654 - [c36]Kalyan Veeramachaneni, Kiarash Adl, Una-May O'Reilly:
Feature Factory: Crowd Sourced Feature Discovery. L@S 2015: 373-376 - [c35]Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O'Reilly, Saman P. Amarasinghe:
Autotuning algorithmic choice for input sensitivity. PLDI 2015: 379-390 - [r2]Lisa Ann Osadciw, Kalyan Veeramachaneni:
Fusion, Decision-Level. Encyclopedia of Biometrics 2015: 747-751 - 2014
- [j7]Pedro Fazenda, Kalyan Veeramachaneni, Pedro U. Lima, Una-May O'Reilly:
Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems. J. Ambient Intell. Smart Environ. 6(6): 675-690 (2014) - [c34]Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, Saman P. Amarasinghe:
OpenTuner: an extensible framework for program autotuning. PACT 2014: 303-316 - [c33]Ignacio Arnaldo, Kalyan Veeramachaneni, Una-May O'Reilly:
Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets. EuroGP 2014: 13-24 - [c32]Dennis Wilson, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly, Hervé Luga:
A continuous developmental model for wind farm layout optimization. GECCO 2014: 745-752 - [i3]Kalyan Veeramachaneni, Sherif A. Halawa, Franck Dernoncourt, Una-May O'Reilly, Colin Taylor, Chuong B. Do:
MOOCdb: Developing Standards and Systems to Support MOOC Data Science. CoRR abs/1406.2015 (2014) - [i2]Kalyan Veeramachaneni, Una-May O'Reilly, Colin Taylor:
Towards Feature Engineering at Scale for Data from Massive Open Online Courses. CoRR abs/1407.5238 (2014) - [i1]Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly:
Likely to stop? Predicting Stopout in Massive Open Online Courses. CoRR abs/1408.3382 (2014) - 2013
- [c31]Kalyan Veeramachaneni, Franck Dernoncourt, Colin Taylor, Zachary A. Pardos, Una-May O'Reilly:
Developing Data Standards and Systems for MOOC Data Science. AIED Workshops 2013 - [c30]Owen Derby, Kalyan Veeramachaneni, Una-May O'Reilly:
Cloud Driven Design of a Distributed Genetic Programming Platform. EvoApplications 2013: 509-518 - [c29]Dennis Wilson, Kalyan Veeramachaneni, Una-May O'Reilly:
Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems. EvoApplications 2013: 519-528 - [c28]Erik Hemberg, Constantin Berzan, Kalyan Veeramachaneni, Una-May O'Reilly:
Introducing graphical models to analyze genetic programming dynamics. FOGA 2013: 75-86 - [c27]Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy, Una-May O'Reilly:
Imprecise selection and fitness approximation in a large-scale evolutionary rule based system for blood pressure prediction. GECCO (Companion) 2013: 153-154 - [c26]Dennis Wilson, Emmanuel Awa, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly:
On learning to generate wind farm layouts. GECCO 2013: 767-774 - [c25]Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, Una-May O'Reilly:
Learning regression ensembles with genetic programming at scale. GECCO 2013: 1117-1124 - [c24]Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy, Una-May O'Reilly:
Efficient training set use for blood pressure prediction in a large scale learning classifier system. GECCO (Companion) 2013: 1267-1274 - [c23]Monica Vitali, Una-May O'Reilly, Kalyan Veeramachaneni:
Modeling Service Execution on Data Centers for Energy Efficiency and Quality of Service Monitoring. SMC 2013: 103-108 - 2012
- [j6]Kalyan Veeramachaneni, Ekaterina Vladislavleva, Una-May O'Reilly:
Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization. Genet. Program. Evolvable Mach. 13(1): 103-133 (2012) - [c22]Kalyan Veeramachaneni, Markus Wagner, Una-May O'Reilly, Frank Neumann:
Optimizing energy output and layout costs for large wind farms using particle swarm optimization. IEEE Congress on Evolutionary Computation 2012: 1-7 - [c21]Dylan Sherry, Kalyan Veeramachaneni, James McDermott, Una-May O'Reilly:
Flex-GP: Genetic Programming on the Cloud. EvoApplications 2012: 477-486 - [c20]Erik Hemberg, Kalyan Veeramachaneni, Una-May O'Reilly:
Graphical models and what they reveal about GP when it solves a symbolic regression problem. GECCO (Companion) 2012: 493-494 - [c19]Erik Hemberg, Kalyan Veeramachaneni, James McDermott, Constantin Berzan, Una-May O'Reilly:
An investigation of local patterns for estimation of distribution genetic programming. GECCO 2012: 767-774 - 2011
- [j5]Kalyan Veeramachaneni, Ekaterina Vladislavleva, Una-May O'Reilly:
Feature extraction from optimization samples via ensemble based symbolic regression. Ann. Math. Artif. Intell. 61(2): 105-123 (2011) - [j4]Kalyan Veeramachaneni:
Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning - CRC Press, 327 pp, ISBN: 978-1-4398-0369-1. Genet. Program. Evolvable Mach. 12(2): 179-180 (2011) - [c18]James McDermott, Una-May O'Reilly, Leonardo Vanneschi, Kalyan Veeramachaneni:
How Far Is It from Here to There? A Distance That Is Coherent with GP Operators. EuroGP 2011: 190-202 - 2010
- [c17]Katya Vladislavleva, Kalyan Veeramachaneni, Una-May O'Reilly, Matt Burland, Jason Parcon:
Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data. EuroGP 2010: 244-255 - [c16]Katya Vladislavleva, Kalyan Veeramachaneni, Matt Burland, Jason Parcon, Una-May O'Reilly:
Knowledge mining with genetic programming methods for variable selection in flavor design. GECCO 2010: 941-948 - [c15]Kalyan Veeramachaneni, Katya Vladislavleva, Matt Burland, Jason Parcon, Una-May O'Reilly:
Evolutionary optimization of flavors. GECCO 2010: 1291-1298 - [c14]Kalyan Veeramachaneni, Katya Vladislavleva, Una-May O'Reilly:
Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression. LION 2010: 251-265
2000 – 2009
- 2009
- [j3]Kalyan Veeramachaneni, Lisa Ann Osadciw:
Situation assessment and autonomous control and optimisation of biometric sensor network. Int. J. Biom. 1(4): 495-524 (2009) - [j2]Kalyan Veeramachaneni, Lisa Ann Osadciw:
Biometric Sensor Management: Tradeoffs in Time, Accuracy and Energy. IEEE Syst. J. 3(4): 389-397 (2009) - [c13]Weihua Gao, Ganapathi Kamath, Kalyan Veeramachaneni, Lisa Ann Osadciw:
A particle swarm optimization based multilateration algorithm for UWB sensor network. CCECE 2009: 950-953 - [c12]Xiang Ye, Kalyan Veeramachaneni, Yanjun Yan, Lisa Ann Osadciw:
Unsupervised learning and fusion for failure detection in wind turbines. FUSION 2009: 1497-1503 - [c11]Nisha Srinivas, Kalyan Veeramachaneni, Lisa Ann Osadciw:
Fusing correlated data from multiple classifiers for improved biometric verification. FUSION 2009: 1504-1511 - [c10]Jagdish Chand Bansal, Kusum Deep, Kalyan Veeramachaneni, Lisa Ann Osadciw:
Information sharing strategy among particles in Particle Swarm Optimization using Laplacian operator. SIS 2009: 30-36 - [c9]Weihua Gao, Kalyan Veeramachaneni, Lisa Ann Osadciw:
Achieving spectrum efficiency through signal design for ultra wide band sensor networks. SIS 2009: 122-128 - [c8]Weihua Gao, Kalyan Veeramachaneni, Ganapathi Kamath, Lisa Ann Osadciw:
A novel ultrawide band locationing system using swarm enabled learning approaches. SIS 2009: 129-136 - [r1]Lisa Ann Osadciw, Kalyan Veeramachaneni: